As computed tomography (CT) achieves higher resolution, noise has been a significant problem from the inception of CT imaging. A major concern in developing a denoising technique is to preserve clinically significant structures of interest. In this regard, although low-pass filtering techniques are well-known effective denoising filters, the processed images tend to lose clinically significant structures. That is, low-pass filtering techniques generally remove high frequencies from the image in order to reduce noise for improving the detectability of large objects in the image. On the other hand, the low-pass filters also reduce the intrinsic resolution of the image as they smooth edges and consequently decrease the detectability of small structures.
Some improvements have been made to the low-pass filtering techniques. For example, some prior art low-pass filtering methods average voxels in local neighborhoods that are within an intensity range depending on the estimated standard deviation of noise throughout the image. Other prior art low-pass filtering techniques are based upon the iteratively estimated standard deviation of noise in a particular local position of CT volume for performing a weighted average between neighboring voxels. These noise reduction methods are not particularly aimed at preserving specific structures of interest.
In contrast, a recent CT denoising approach involves anisotropic diffusion as a denoising filter whose diffusion coefficient is made image dependent. Certain image structures can be directionally smoothed with anisotropic diffusion. Since exemplary CT noise reduction methods using anisotropic diffusion improve only certain predetermined structures such as elongated-shaped vessels, these methods are not suited for image enhancement in preserving more general structures or other clinically significant structures in reconstructed CT image data.
Despite computationally complex implementation, anisotropic smoothing techniques have gained attention to reduce noise while preserving structures of interest. While some prior art anisotropic smoothing techniques improved in preserving small structures of interest in image data, these prior art techniques undesirably require to determine an importance map, which indicates potentially relevant structures that should be preserved. Anisotropic diffusion denoising methods and systems thus remain desired to be improved in preserving clinically significant structures in reconstructed images using a variety of modalities including CT and more broadly, multi-dimensional signals.